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Most side projects start with a simple frustration. Mine started with a banking app. One of my banks had a feature I really liked. It automatically categorized transactions and showed spending breakdowns in graphs and charts. For the first time, I could easily see how much I spent on restaurants, groceries, transport, subscriptions, and other categories. The problem was that only one of my banks offered this feature. Like many people, I use multiple bank accounts, credit cards, and savings accounts. Two of my other banks provided little more than a long list of transactions. If I wanted a complete picture of my finances, I had to switch between apps and manually piece everything together. As a software engineer, my first instinct was obvious: "Why don't I just build this myself?" That idea eventually became MyVault . The Original Goal The first version of the project was surprisingly simple. I wanted users to: Upload bank statements Extract transaction data Automatically categorize spending View useful charts and reports The goal wasn't budgeting. It wasn't investment tracking. It wasn't accounting. I simply wanted a single place where I could see spending across all of my bank accounts. Once I started building, however, I realized there was a much more interesting opportunity. If all transaction data was already extracted and structured, why not allow users to ask questions about their finances? Instead of searching through transactions manually, users could simply ask: How much did I spend on restaurants last year? What subscriptions am I paying for? Which categories increased the most this month? How much did I spend while traveling? That's when MyVault started evolving from a reporting tool into an AI-powered financial assistant. Building as a Solo Developer One of the biggest challenges wasn't technology. It was building everything alone. When you're working on a side project, you don't just write code. You become responsible for everything: Product decisions B

When I started learning Excel as part of my Data Science & Analytics course, I assumed it was just a tool for creating tables and performing basic calculations. After spending a week exploring its features, I quickly realized that Excel is much more powerful than I thought. Almost every organization generates data. Businesses track sales, schools monitor student performance, hospitals manage patient records, and marketers analyze campaign results. Before data can be analyzed, it needs to be organized, cleaned, and summarized—and that's where Excel comes in. In this article, I'll share some of the Excel concepts I've learned so far and how they're used in real-world data analysis. Understanding the Excel Workspace Before working with data, it's important to understand the basic structure of Excel. When you open Excel, you're working inside a workbook . A workbook can contain multiple worksheets (often called sheets), which help organize different sets of data. At the top of the screen is the Ribbon , which contains tabs such as Home, Insert, Page Layout, Formulas, Data, and View. The Ribbon acts like a control center where you can access Excel's tools and features. Rows run horizontally and are identified by numbers, while columns run vertically and are identified by letters. The intersection of a row and column is called a cell , where data is entered. At first, all these parts seemed overwhelming, but after using Excel regularly, navigating through them has become much easier. The Different Types of Data in Excel One of the first things I learned is that not all data is the same. Excel commonly works with: Text data (names, product categories, locations) Numeric data (sales figures, quantities, prices) Date and time data (order dates, deadlines) Logical data (TRUE or FALSE values) Understanding data types is important because Excel treats each type differently when performing calculations and analysis. Number Formats Matter More Than I Expected Another concept that

The Problem If you've ever audited your Windows network traffic during a boot-up sequence, you know the truth: there's a "blind spot." Between the moment your network drivers initialize and your VPN/WireGuard tunnel actually establishes, your traffic is leaking. Many third-party solutions exist, but they are often bloated, use proprietary binaries, or act as black boxes. I wanted something transparent, native, and bulletproof. The Solution: WG-KillSwitch I developed a pure PowerShell-based kill switch architecture. It doesn't rely on third-party libraries—it uses native Windows system components to enforce security. Key Architectural Features: Zero-Trust Firewall Matrix: Hardens the system by blocking all outbound traffic by default, allowing only authenticated tunnel traffic. WMI Persistent Watchdog: Unlike standard scripts that can be killed via Task Manager, this project uses WMI Event Subscriptions. If the watchdog process is terminated, Windows itself immediately respawns it. Resilience: Survives hard reboots, modem resets, and Windows service cycling. Resilience & Leak Testing I've put this through a gauntlet of tests: Forced Reboots: Zero leaks detected during driver load. Process Termination: The WMI engine restores the protection in milliseconds. Dynamic Network Resets: The firewall matrix remains active regardless of adapter status. Let's Collaborate This is open source, transparent, and built for the community. I'm looking for security audits and feedback. Check out the source code, open an issue, or submit a PR: https://github.com/ryderlacin-pixel/Windows-WireGuard-KillSwitch

Last year, I examined RUM indexes within this series on multi-key indexing, demonstrating that they cannot substitute MongoDB's compound indexes for sorted queries. A year later, Microsoft has fixed this in the DocumentDB extension for PostgreSQL with an Extended RUM index that preserves the ordering of the keys, allowing an ordered scan rather than a bitmap scan. Let's revisit our pagination query to see how it performs now. I start a container with the latest DocumentDB (version v0.112-0 from May 26, 2026): docker run -d --name documentdb-local -p 10260:10260 -p 9712:9712 ghcr.io/documentdb/documentdb/documentdb-local:latest --username franck --password franck --start-pg I can connect to PostgreSQL on port 9712, where many extensions are installed, including the extended RUM index: docker exec -it documentdb-local psql -p 9712 postgres psql ( 17.10 ( Debian 17.10-1.pgdg13+1 )) Type "help" for help. postgres = # \dx List of installed extensions Name | Version | Schema | Description ------------------------- +---------+------------+------------------------------------------------------------ documentdb | 0.112-0 | public | API surface for DocumentDB for PostgreSQL documentdb_core | 0.112-0 | public | Core API surface for DocumentDB on PostgreSQL documentdb_extended_rum | 0.112-0 | public | DocumentDB Extended RUM index access method pg_cron | 1.6 | pg_catalog | Job scheduler for PostgreSQL plpgsql | 1.0 | pg_catalog | PL/pgSQL procedural language postgis | 3.6.3 | public | PostGIS geometry and geography spatial types and functions tsm_system_rows | 1.0 | public | TABLESAMPLE method which accepts number of rows as a limit vector | 0.8.2 | public | vector data type and ivfflat and hnsw access methods ( 8 rows ) postgres = # I can also connect to the MongoDB-compatible API: docker exec -it documentdb-local mongosh -u franck -p franck 'mongodb://localhost:10260/?tls=true&tlsAllowInvalidCertificates=true' Current Mongosh Log ID: 6a0b3b537d2a1c3471d1a7ba Connecting to: mo

If you ask it to create a recipe, you can click plus and minus buttons to change the amount of portions. You can connect it to other apps like canva. It hallucinates WAY less, and it explains ilvery clearly. submitted by /u/OkComputer_13 [link] [留言]

I have been experimenting with LLMs to see how well they navigate highly cross-referenced texts like the Bible. Standard models often hallucinate verses or lose historical context. To try and fix this, I built a free app called Biblians (no ads, no paywalls). I built it specifically for people who have questions they might hesitate to ask in person, or who simply want a 1-click way to explain a verse. While testing it, I discovered a fascinating denominational bias that is still lingering and changes depending entirely on the language you use: In English: It is Protestant-leaning. It praises Luther, saying things like, "Martin Luther sought to return the Church to the truth of God's Word." In Spanish, French, or Portuguese: It is Catholic-leaning. It condemns Luther's actions, stating: "...trajo confusión..." (...brought confusion...). Has anyone else noticed how drastically the training data changes the core bias based on the language prompted? I would love for this community to test the app, look for other linguistic biases, or just try to break the AI's logic. You can experiment with it here: https://play.google.com/store/apps/details?id=com.biblians.app Let me know what weird outputs you get! submitted by /u/Snorlax_lax [link] [留言]

The original babel-plugin-transform-flow-strip-types hasn't been updated in 9 years and breaks silently in Babel 7 and 8 environments. The fix I published a maintained fork that works as a drop-in replacement: npm install --save-dev babel-plugin-transform-flow-strip-types-maintained Then update your .babelrc: { "plugins": ["transform-flow-strip-types-maintained"] } That's it. No other changes needed. What's fixed Babel 7 and 8 peer dependency conflicts Missing syntax plugin declaration Deprecated visitor patterns allowDeclareFields support Automated migration If you want to update your entire project automatically: npx flow-strip-migrate . This updates your package.json and babel config in one command. More info: https://flowstrip.netlify.app npm: https://www.npmjs.com/package/babel-plugin-transform-flow-strip-types-maintained

May 25, 2026. This is not just another date on my calendar. This marks the beginning of one of the biggest professional transitions of my life. After nearly a decade working in the world of IT infrastructure, technical support, networking, field engineering, and systems operations, I’ve made a decision that has been building in my mind for some time: I am transitioning into Data Analytics. And this is where I document that journey—publicly, honestly, and in real time. Not when I become an expert. Not when I feel “ready.” Not when everything looks polished. I’m starting now. Because real growth is rarely clean, predictable, or perfectly planned. Sometimes it starts with one uncomfortable decision: To leave what you already know… and step into what your future requires. Where My Journey Started Before data, before dashboards, before writing my first SQL query or building my first analytics project—my career started in the trenches of IT. For the past 10 years, I’ve built my career solving real technical problems across businesses, organizations, schools, offices, and field operations. My world has been cables, routers, networks, system failures, installations, troubleshooting, and making technology work where others saw complexity. Over the years, I’ve worked deeply in: Computer troubleshooting and hardware diagnostics Printer setup, configuration, and enterprise support Wi-Fi deployment and hotspot installations LAN design and structured network deployment Fiber optic installations and network termination Data cabling and structured cabling systems CCTV surveillance installation and maintenance Alarm systems and electronic security integration Intelligent security systems Electric fence installations and perimeter protection systems Router, switch, and access point configuration End-user support and enterprise technical troubleshooting Systems maintenance and operational support I’ve spent years on ladders, in server rooms, inside offices, on construction sites, insi

OK.. so I've had this reasonable PC sitting under my desk for ages.. NOT working because of some reason or other. But it was my baby as is housed in a lovely Soprano DX silver brushed case. SO, I swapped out the old HDD for a couple of SSDs (a couple of mirrored OS disks and a large 2TB storage disk) I swapped out the Nvidia 780ti graphics card for a couple of OG Nvidia 1080ti's. I pulled the whole thing to bits.. repasted the northbridge chip, southbridge chip and central CPU. Upgraded the fans to push pull the CPU heatsink. Wrapped ALL cables in mesh and it's so lovely now. Installed Windows 10 Pro. Installed the Nvidia App. Installed CrystalDiskInfo and all is sweet 😄 EXCEPT... I'd like to use this old bangin box for an HG AI server... now I have read that ALL LLMs need this thing called AVX (Advanced Vector Extensions) I didn't even know that was a THING! So even though I have 22Gb worth of GPU sitting there that I was going to point everything to, because I have a lame ass QX6700 CPU sitting on a kickass D975XBX2 (BadAxe2) main board I CAN NOT fulfill my wish for this OG box to be a headless source of awesomeness sitting in it's home under my desk supplying me with a home grown AI. IS THERE ANYTHING I CAN DO?!?!?! Surely after all this time of parts getting munched by AI farms a plenty people have been using what's around to do what they will... Does anyone know of anything I can do apart from just look at it running at 25 degrees aircooled humming along so lovely... it NEEDS purpose!!! 😄 Cheers and thanks all NB submitted by /u/Independent-Sound196 [link] [留言]

10 year senior dev, vibe coded this in 4 weeks and counting. Something like this would have taken me a year+ before and ive always been a 10x dev. I built this along side my day job (gov contractor dev). Feel free to check it out! https://imgur.com/a/F6OINKR⁠ Game Title: Roguelite MMO Playable Link: https://roguelite-mmo.com/⁠ Platform: PC / Web Description: Roguelite MMO is a browser-based RPG/MMO project built around dungeon runs, exploration, gear progression, PvP, quests, loot, and character building. The game is still in beta and active development, with the latest update adding new side activities and progression options. Latest update: The new Casino is now live, giving players more ways to spend gold, take risks, and chase rewards between dungeon runs and exploration. Horse racing and horse taming have also been added. Players can race horses, bet on races, and work toward collecting better horses over time. Fishing is now available too, adding a more relaxed activity with its own rewards while exploring the world. The core loop is still being refined, but the current focus is making sure players understand what they earned, where important items come from, what to do next, and whether the early gameplay loop feels worth continuing after the first few minutes. Free to play submitted by /u/HeadHunterX223 [link] [留言]

This is a submission for the GitHub Finish-Up-A-Thon Challenge. I’ll keep this short since the repo...

I had a work version of GPT do a very simple spreadsheet summary task for me yesterday. It took it 5 minutes to do it. I could probably have done it myself in 30 or so minutes. The heavily subsidised token cost of that task? 10 dollars. That's with a 10x subsidy. The actual compute cost was about 100 dollars. There's something seriously wrong there. It's going to crash and crash HARD. if people think i'm lying or are just interested. The spreadsheet had 45 sheets. Each sheet had roughly 500 x 50 populated cells. Formatting was not exactly standard across all sheets. The prompt was something like "there is labelled column in each sheet, give me a simple list of all the items from all the sheets in that column and ignore duplicates." We can chose which model to use. The model I chose was one of the newer ones, I honestly can't remember which one, possibly GPT 5.5. It took 5 minutes or more to so and the stated cost for the task was 10 dollars, possibly even more. I can't recall the token amount. submitted by /u/Complete-Sea6655 [link] [留言]

I maintain an open-source GitHub Action called vorsken. It does one thing: scan the diff on a pull request with Semgrep, apply a fixed policy, and return BLOCK, FLAG, or PASS. No dashboard, no model that drifts over time. Rules at ERROR/HIGH/CRITICAL severity block the merge, WARNING/MEDIUM flag it, the rest pass. Same diff, same verdict. The usual pitch for a tool like this is that it catches the SQL injection your AI assistant wrote. I wanted to see what it actually catches against real assistant output, so I generated 28 functions and ran them through. The test Seven backend tasks: a FastAPI upload endpoint, a URL-fetch helper, JWT auth, a SQL filter, an ImageMagick subprocess call, a LangChain file agent, and a LangChain RAG pipeline. I generated each one four times, with ChatGPT (GPT-5.5 Instant), Claude Code (Opus 4.8), Claude Code plus the security-guidance plugin, and Cursor (Composer 2.5). Single-shot, neutral prompt, no security hints. Then I scanned all 28 with the same ruleset. I'm reporting which rule fired on which file, not whether some model thinks the code is safe. That part you can reproduce. Task ChatGPT Claude Code + plugin Cursor Verdict file upload — — — — PASS url fetch (SSRF) ssrf ssrf ssrf — FLAG / Cursor PASS jwt auth api8 api8 — — BLOCK / 2 PASS sql filter — — — — PASS imagemagick — — — — PASS fs agent — overperm — — 1 BLOCK / 3 PASS rag dangerous dangerous dangerous dangerous BLOCK 7 BLOCK, 3 FLAG, 18 PASS across 28 functions. The basics were fine SQL filter, ImageMagick, file upload: clean on every tool. The SQL was parameterized, the subprocess calls passed argument lists instead of shell strings, the uploads weren't doing anything reckless. If you still expect current models to spray SQL injection across a straightforward CRUD task, they don't. On conventional work they get it right. Two of the flags are soft. The JWT api8 hits landed on a SECRET_KEY = "CHANGE_ME" placeholder, which you can read as a false positive or as a gate doing i

Multi-Model AI API Routing: Cut Costs Without Sacrificing Quality Problem: You're building an AI-powered app, but relying on a single model (like GPT-4) for every request is burning through your budget. Simple tasks like summarization or classification don't need a heavyweight model, yet you're paying premium prices for them. Solution: Route requests intelligently to the cheapest model that can handle each task. This is multi-model AI API routing, and it can cut your costs by 60-80% while maintaining output quality. Prerequisites Python 3.8+ API keys for at least 2 AI providers (e.g., OpenAI, Anthropic, or NovaAPI) Basic understanding of async/await in Python Step 1: Define Your Routing Strategy First, create a routing configuration that maps task complexity to model tiers: # router_config.py ROUTING_CONFIG = { " simple " : { " models " : [ " nova-1-fast " , " gpt-3.5-turbo " ], " cost_per_token " : 0.0001 , " max_tokens " : 500 , " tasks " : [ " summarization " , " classification " , " entity_extraction " ] }, " medium " : { " models " : [ " nova-1-medium " , " gpt-4-mini " ], " cost_per_token " : 0.0005 , " max_tokens " : 2000 , " tasks " : [ " code_generation " , " translation " , " sentiment_analysis " ] }, " complex " : { " models " : [ " nova-1-pro " , " gpt-4 " ], " cost_per_token " : 0.002 , " max_tokens " : 4000 , " tasks " : [ " reasoning " , " creative_writing " , " complex_qa " ] } } Step 2: Build the Router Now implement the core routing logic with fallback capabilities: # ai_router.py import asyncio from typing import Dict , List , Optional import time class AIRouter : def __init__ ( self , config : Dict , api_keys : Dict [ str , str ]): self . config = config self . api_keys = api_keys self . metrics = { " cost " : 0 , " requests " : 0 , " failures " : 0 } async def route_request ( self , task : str , prompt : str ) -> str : """ Route request to appropriate model based on task complexity. """ tier = self . _classify_task ( task ) models = self . confi

Every time I started a side project, I rebuilt the same five things before I wrote a single line of the actual idea: auth, a database, file uploads, a deploy pipeline, and TLS. Different domain, same plumbing. By the third project I was copy-pasting my own docker-compose.yml from a folder two repos over and renaming things until they stopped erroring. So I stopped. I froze that plumbing into one boilerplate — PocketBase + Next.js + Caddy on a single cheap VPS — and now I ship every side project on it. Same shape every time: clone, rename, write the part that's actually new, push. The thing I care about most isn't the speed, though. It's that I stopped paying for it. A handful of real projects now run on this setup for a few euros a month, total — not a stack of per-service SaaS bills that each want $25 here and $20 there before you've shipped anything. No Vercel seat, no managed Postgres, no Auth-as-a-Service, no object-storage line item. Here's the whole thing. Why this stack The trick that makes the cost collapse is PocketBase . It's a single Go binary that gives you, in one process: Auth — email/password, OAuth, the works, with a real users collection A database — SQLite, with a schema you manage from an admin UI Realtime — subscribe to collection changes over SSE File storage — uploads handled, with on-the-fly thumbnails An admin dashboard — at /_/ , for free That's four or five separate SaaS products collapsed into one binary that runs anywhere and stores everything in a folder. Compared to wiring up Supabase or Firebase, the mental model is tiny: it's one process and one data directory. Back up the directory and you've backed up the entire app — database, uploaded files, auth tokens, all of it. For the front I use Next.js (App Router, React Server Components) because that's where I'm fastest, and Caddy as the reverse proxy because it gets you automatic HTTPS with zero config — it provisions and renews Let's Encrypt certificates on its own. And it all lives on